标题： MedEBench：回顾医学领域的文本指导图像编辑 显示英文标题

标题： MedEBench: Revisiting Text-instructed Image Editing on Medical Domain

摘要： 文本引导的图像编辑在自然图像领域取得了快速进展，但其在医学影像中的应用仍受到限制且缺乏标准化评估。 临床上，此类编辑有望模拟手术结果、创建个性化教学材料以及增强患者沟通。 为弥合这一差距，我们推出了MedEBench，这是一个全面的基准，用于评估文本引导的医学图像编辑。 它由来自13个解剖区域的70项任务、共计1,182个临床来源的图像-提示三元组组成。 MedEBench提供了三个关键贡献：（1）一个涵盖编辑准确性、上下文保留和视觉质量的临床相关评估框架，并附有预期变化和感兴趣区域（ROI）掩码的详细描述；（2）对七种最先进的模型进行系统比较，揭示常见失败模式；以及（3）基于注意力定位的失败分析协议，通过注意力图与ROI之间的IoU来识别错误定位。 MedEBench为开发和评估可靠且具有临床意义的医学图像编辑系统奠定了坚实的基础。 项目网站：https://mliuby.github.io/MedEBench_Website/ 显示英文摘要

摘要： Text-guided image editing has seen rapid progress in natural image domains, but its adaptation to medical imaging remains limited and lacks standardized evaluation. Clinically, such editing holds promise for simulating surgical outcomes, creating personalized teaching materials, and enhancing patient communication. To bridge this gap, we introduce MedEBench, a comprehensive benchmark for evaluating text-guided medical image editing. It consists of 1,182 clinically sourced image-prompt triplets spanning 70 tasks across 13 anatomical regions. MedEBench offers three key contributions: (1) a clinically relevant evaluation framework covering Editing Accuracy, Contextual Preservation, and Visual Quality, supported by detailed descriptions of expected change and ROI (Region of Interest) masks; (2) a systematic comparison of seven state-of-the-art models, revealing common failure patterns; and (3) a failure analysis protocol based on attention grounding, using IoU between attention maps and ROIs to identify mislocalization. MedEBench provides a solid foundation for developing and evaluating reliable, clinically meaningful medical image editing systems. Project website: https://mliuby.github.io/MedEBench_Website/